This guide offers five essential tips for writing effective GitHub Copilot custom instructions, covering project overview, tech stack, coding guidelines, structure, and resources, to help developers get better code suggestions.
TraceRoot accelerates the debugging process with AI-powered insights. It integrates seamlessly into your development workflow, providing real-time trace and log analysis, code context understanding, and intelligent assistance. It offers both a cloud and self-hosted version, with SDKs available for Python and JavaScript/TypeScript.
This article discusses Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data. It details the key components of MCP, its benefits (improved interoperability, future-proofing, and modularity), and its adoption in open-source agent frameworks like LangChain, CrewAI, and AutoGen. It also includes case studies of MCP implementation at Block and in developer tools.
frozen-in-time version of our Paper Finder agent for reproducing evaluation results. This repo contains the code for the standalone Paper Finder agent. PaperFinder is our paper-seeking agent, which is intended to assist in locating sets of papers according to content-based and metadata criteria.
MCP-Universe is a comprehensive benchmark designed to evaluate LLMs in realistic tasks through interaction with real-world MCP servers across 6 core domains and 231 tasks. It highlights the challenges of long-context reasoning, unfamiliar tool spaces, and cross-domain variations in LLM performance.
A workshop that teaches you how to build your own coding agent. Similar to Roo code, Cline, Amp, Cursor, Windsurf or OpenCode.
Vercel proposes using
<script type="text/llms.txt"> to include inline instructions for LLMs directly in HTML responses, particularly for access control and agent navigation.
<pre>
<script type="text/llms.txt">
## Note to agents accessing this page:
This page requires authentication to access. Automated agents should use a
Vercel authentication bypass token to access this page.
The easiest way to get a token is using the get_access_to_vercel_url or ...
</script>
</pre>
<script type="text/llms.txt"> to include inline instructions for LLMs directly in HTML responses, particularly for access control and agent navigation.
<pre>
<script type="text/llms.txt">
## Note to agents accessing this page:
This page requires authentication to access. Automated agents should use a
Vercel authentication bypass token to access this page.
The easiest way to get a token is using the get_access_to_vercel_url or ...
</script>
</pre>
> Important: We do not accept PRs for core features.
DockaShell is an MCP (Model Context Protocol) server that gives AI agents isolated Docker containers to work in. Each agent gets its own persistent environment with shell access, file operations, and full audit trails. It aims to remove limitations of current AI assistants like lack of persistent memory, tool babysitting, limited toolsets, and no self-reflection, enabling self-evolving agents, continuous memory, autonomous exploration, and meta-learning.
MCP UI extends the Model Context Protocol to enable AI agents to return fully interactive UI components, solving the challenge of delivering commerce experiences that require visual and interactive elements like product selectors and cart flows. It uses an intent-based messaging system to maintain agent control.
